Test Post¶

Basic Markdown Functions¶

Text¶

italics

bold

~strikethrough~

links

blockquotes

Lists:¶

Ordered¶

One
Two

Unordered¶

Cat
Bat

Checkboxes¶

[x] Checkboxes
[ ] Theorem environments

(For some reason, as of the time of writing the checkboxes also have bullets beside them, which is not desired.)

Unicode text¶

μῆνιν ἄειδε θεὰ Πηληϊάδεω Ἀχιλῆος

οὐλομένην, ἣ μυρί᾽ Ἀχαιοῖς ἄλγε᾽ ἔθηκε,

πολλὰς δ᾽ ἰφθίμους ψυχὰς Ἄϊδι προΐαψεν

ἡρώων, αὐτοὺς δὲ ἑλώρια τεῦχε κύνεσσιν

5οἰωνοῖσί τε πᾶσι, Διὸς δ᾽ ἐτελείετο βουλή,

ἐξ οὗ δὴ τὰ πρῶτα διαστήτην ἐρίσαντε

Ἀτρεΐδης τε ἄναξ ἀνδρῶν καὶ δῖος Ἀχιλλεύς.

Images from the web¶

Mathematics¶

The flavor of Markdown I'm using supports $\LaTeX$ typesetting of mathematics via MathJax.

For instance, here are Maxwell's equations:

\begin{align} \nabla \times \vec{\mathbf{B}} -\, \frac1c\, \frac{\partial\vec{\mathbf{E}}}{\partial t} & = \frac{4\pi}{c}\vec{\mathbf{j}} \\ \nabla \cdot \vec{\mathbf{E}} & = 4 \pi \rho \\ \nabla \times \vec{\mathbf{E}}\, +\, \frac1c\, \frac{\partial\vec{\mathbf{B}}}{\partial t} & = \vec{\mathbf{0}} \\ \nabla \cdot \vec{\mathbf{B}} & = 0 \end{align}

It doesn't seem to support theorem environments, alas.

Python code¶

In [1]:

# a comment

import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go 
from urllib.request import urlopen
import json

Data frames¶

In [2]:

df = px.data.iris()
df

Out[2]:

	sepal_length	sepal_width	petal_length	petal_width	species	species_id
0	5.1	3.5	1.4	0.2	setosa	1
1	4.9	3.0	1.4	0.2	setosa	1
2	4.7	3.2	1.3	0.2	setosa	1
3	4.6	3.1	1.5	0.2	setosa	1
4	5.0	3.6	1.4	0.2	setosa	1
...	...	...	...	...	...	...
145	6.7	3.0	5.2	2.3	virginica	3
146	6.3	2.5	5.0	1.9	virginica	3
147	6.5	3.0	5.2	2.0	virginica	3
148	6.2	3.4	5.4	2.3	virginica	3
149	5.9	3.0	5.1	1.8	virginica	3

150 rows × 6 columns

Charts and Graphs¶

Line Charts¶

I'm using Plot.ly for my graphing, because it offers an intuitive, easy-to-use API and because it supports interactive visualizations.

In [3]:

# example graph using the Iris dataset
df = px.data.iris()
fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species", marginal_y="violin",
           marginal_x="box", trendline="ols", template="simple_white")
fig.show()

Scatterplots¶

In [4]:

# the same dataset in 3d

fig = px.scatter_3d(df, x='sepal_length', y='sepal_width', z='petal_width',
              color='species')
fig.show()

Area charts¶

Another example visualizing topographical data with a color scheme.

In [6]:

# 3D surface example plot from the plotly docs
# Read data from a csv
z_data = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/api_docs/mt_bruno_elevation.csv')

fig = go.Figure(data=[go.Surface(z=z_data.values)])

fig.update_layout(title='Mt Bruno Elevation', autosize=False,
                  width=500, height=500,
                  margin=dict(l=65, r=50, b=65, t=90))

fig.show()

Geodata visualizations¶

Another cool chloropleth example from the docs that utilizes GeoJSON to visualize the unemployment rate in the US by county.

In [7]:

with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:
    counties = json.load(response)


df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv",
                   dtype={"fips": str})

fig = px.choropleth_mapbox(df, geojson=counties, locations='fips', color='unemp',
                           color_continuous_scale="Viridis",
                           range_color=(0, 12),
                           mapbox_style="carto-positron",
                           zoom=3, center = {"lat": 37.0902, "lon": -95.7129},
                           opacity=0.5,
                           labels={'unemp':'unemployment rate'}
                          )
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()